Papers by Keyword: Rough Set

Abstract: Lead, Mercury and Cadmium etc as the main evaluation index of heavy metal pollution established relational data model. The rough set theory is introduced, use the existing algorithm (Combinatorial Completer) to fill the missing value. After data preprocessing, use the DBMAS algorithm proposed in the paper to calculate the important degree of heavy metal pollution factors, in order to provides a more objective evaluation index weight for evaluation of heavy metal pollution.

Abstract: Computer teaching should emphasize the engineering practicality, creativity, and pay more attention to the project and its application. It is important to evaluate the teaching effect. An evaluation system and corresponding algorithm are presented in this paper. There is a certain correlation between some evaluation factors, and the factors can be divided into key factors and secondary factors by rough set. The evaluation algorithm based on the key factors can reduce redundant factors and improve the efficiency. We designed a clustering algorithm based on fuzzy set on evaluation entities, which can reduce the dada size and improve the accuracy of the algorithm. Through the example analysis the algorithm of factors reduction based on rough set and clustering method based on fuzzy set are described in detail.

Abstract: With the rising of the supply chain in all areas, trust issues become more and more concern. An applied-information technology with trust risk evaluation method is presented. Trust risk rating index system is built after analyzing trust risks in the supply chain. The key indexes of trust risk indexes are selected based on rough set theory, and a simplified trust risk evaluation index system is constituted. The weight of each index is calculated by rough set theory. Supply chain trust risk is assessed based on the determined index system, index weight and advantages of the uncertain multiple attribute decision making methods, which provide the basis for trust of supply chain risk prevention.